Modern electronics are ubiquitous in healthcare. For example, monitoring devices often include electronic components and algorithms to sense, measure, and monitor living beings. Monitoring equipment can measure vital signs such as respiration rate, oxygen level in the blood, heart rate, and so on. Not only are monitoring devices used in the clinical setting, monitoring devices are also used often in sports equipment and consumer electronics.
One important measurement performed by many of the monitoring equipment is heart rate, typically measured in beats per minute (BPM). Athletes use heart rate monitors to get immediate feedback on a workout, while health care professionals use heart rate monitors to monitor the health of a patient. Many solutions for measuring heart rate are available on the market today. For instance, electronic heart rate monitors can be found in the form of chest straps and watches. However, these electronic heart rate monitors are often not very accurate, due to a high amount of noise present in the signals provided by the sensors of these monitors. The noise is often caused by the fact that the user is moving and also by the lack of secure contact between the monitor and the user. This noisy environment often leads to an irregular, inaccurate or even missing readout of the heart rate.
Overview
Heart rate monitors are plagued by noisy photoplethysmography (PPG) data, which makes it difficult for the monitors to output a consistently accurate heart rate reading. Noise is often caused by motion. Using known methods for processing accelerometer readings that measure movement to filter out some of this noise may help, but not always. The present disclosure describes an improved front-end technique (time-domain interference removal) based on using adaptive linear prediction on accelerometer data to generate filters for filtering the PPG signal prior to tracking the frequency of the heartbeat (heart rate). The present disclosure also describes an improved back-end technique based on steering the frequency of a resonant filter in order to track the heartbeat. Implementing one or both of these techniques leads to more accurate heart rate measurements.
According to an improved front-end technique, a method for assisting identification and/or tracking of a frequency of a heartbeat signal present in one or more first signals generated by one or more sensors in a noisy environment is disclosed. The method includes steps of a) receiving data samples of a first signal; b) receiving data samples of a second signal indicative of motion of the one or more sensors; c) applying adaptive linear prediction to at least a portion of the data samples of the second signal to determine coefficients of an adaptive linear filter configured to filter (i.e., substantially attenuate) at least a part of noise content present in the second signal from a signal indicative of the motion of the one or more sensors; and d) generating a filtered first signal by subtracting from the first signal a signal generated by applying a filter comprising the determined coefficients to the first signal (In other words, the first signal is filtered based on processing the data samples of the first signal with a filter comprising the determined coefficients, where the step of filtering the first signal substantially attenuates signal content corresponding to the motion of the one or more sensors).
According to an improved back-end technique, a method for extracting a frequency of a heartbeat signal present within an input signal generated by one or more sensors using a structure implementing a first filter (FILTERA) and a second filter (FILTERB) is disclosed. In some embodiments, the structure may be configured to implement the first and second filters as a separate filters comprising their respective, different, components. In other embodiments, the structure may be a resource-shared structure where at least some, but possibly all, of the components used in implementing the first filter are also used (i.e. shared) in implementing the second filter, and vice versa. Regardless of this, the first filter and the second filter are filters have a frequency F_RES at which the amplitudes of their outputs are substantially equal and for which, above the frequency F_RES, the amplitude of the first filter becomes larger than the amplitude of the second filter, and, below the frequency F_RES, the amplitude of the second filter becomes larger than the amplitude of the first filter. The method includes steps of: a) determining an amplitude (A) of an output generated by the first filter in response to the first filter receiving the input signal and one or more filter control parameters; b) determining an amplitude (B) of an output generated by the second filter in response to the second filter receiving the input signal and the one or more filter control parameters; c) updating the filter control parameters based on the amplitudes A and B and proceeding to step a); d) determining the frequency of the heartbeat signal based on the one or more filter control parameters.